Krištof Oštir

Sky-View Factor as a Relief Visualization Technique

Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models

Aerial mapping and remote sensing takes another step forward with this method of modelling lidar data. The usual form of presentation, hill shade, uses a point source to show up surface features. Sky-view factor simulates diffuse light by computing how much of the sky is visible from each point. The result is a greatly improved visibility — as shown here by its use on a test site of known topography in Slovenia.

Solar radiation modelling

The Sun is the main energy source of the life on the Earth. Thus, solar radiation energy data and models are important for many areas of research and applications. Many parameters influence the amount of solar energy at a particular standing point of the Earths surface; therefore, many solar radiation models were produced in the last few years. Solar radiation energy depends mostly on incidence angle, which is defined by astronomical and surface parameters. Our solar radiation model is based on defining incidence angle by computing normal-to-the-surface tangent plane and direction of the Sun. If a part of the surface is in the shadow, it receives lesser energy than sunny areas. That is why shadow determination is an important part of the model. The sky is usually not completely clear, so meteorological parameters had to be integrated into the model. Meteorological model distinguishes among direct and diffuse Sun radiation. The model was tested and implemented for the whole Slovenia and it was also compared with previous studies. Case study surface data were calculated from the DEM with a 25m resolution. The astronomical data, which were required for virtual Sun motion simulation around the Earth, were derived from the astronomical almanac. Meteorological data were acquired from observed mean values on 24 meteorological stations between 1961 and 1990. All calculations were made for hours and decades and finally, the annual quasiglobal radiation energy, which is the energy received by inclined plane from the Sun in one year, was calculated from the sum of all the energies of all the decades.

Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality

Abstract Segmentation, the first step of object-based classification, is crucial to the quality of the final classification results. A poor quality of the segmentation leads directly to a low quality of the classification. Therefore, it is very important to evaluate the segmentation results using quantitative methods and to know how to obtain the best results. To obtain the best possible segmentation results, it is important to choose the right input data resolution as well as the best algorithm and its parameters for a specific remote sensing application. The impact of the segmentation algorithm, the parameter settings, as well as the spatial and spectral resolution of the data is investigated. To describe these impacts, we performed more than 70 segmentations of a Worldview-2 image. The impact of the spectral resolution was tested with 10 combinations of data on different spectral channels, and the impact of the spatial resolution was tested on an original and downsampled test image to four different spatial resolutions. We investigated these impacts on the segmentation of objects that belong to the classes urban, forest, bare soil, vegetation, and water. The impacts on the segmentation are described using a common methodology for the evaluation of segmentation.

Object-Based Image Analysis of VHR Satellite Imagery for Population Estimation in Informal Settlement Kibera-Nairobi, Kenya

Kibera (edge region within the Nairobi) is the biggest informal settlement in Kenya, and one of the biggest in Africa. The population estimates vary between 170,000 and 1 million and are highly debatable. What is certain is that the area is large (roughly 2.5 km2), host at least hundreds of thousands people, is informal and self-organized, stricken by poverty, disease, population increase, environmental degradation, corruption, lack of security and often overlooked but extremely important – lack of information which all contribute to lack of basic services such as access to safe water, sanitation, health care and formal education.

Automatic Geometric Processing for Very High Resolution Optical Satellite Data Based on Vector Roads and Orthophotos

In response to the increasing need for fast satellite image processing SPACE-SI developed STORM—a fully automatic image processing chain that performs all processing steps from the input optical images to web-delivered map-ready products for various sensors. This paper focuses on the automatic geometric corrections module and its adaptation to very high resolution (VHR) multispectral images. In the automatic ground control points (GCPs) extraction sub-module a two-step algorithm that utilizes vector roads as a reference layer and delivers GCPs for high resolution RapidEye images with near pixel accuracy was initially implemented. Super-fine positioning of individual GCPs onto an aerial orthophoto was introduced for VHR images. The enhanced algorithm is capable of achieving accuracy of approximately 1.5 pixels on WorldView-2 data. In the case of RapidEye images the accuracies of the physical sensor model reach sub-pixel values at independent check points. When compared to the reference national aerial orthophoto the accuracies of WorldView-2 orthoimages automatically produced with the rational function model reach near-pixel values. On a heterogeneous set of 41 RapidEye images the rate of automatic processing reached 97.6%. Image processing times remained under one hour for standard-size images of both sensor types.

Detecting flooded areas with machine learning techniques: case study of the Selška Sora river flash flood in September 2007

Abstract Floods seem to appear with increased frequency from one year to another. They create great damage to property and in some cases even result in lost lives. However, a quick and effective response by rescue services can greatly reduce the consequences. Machine learning techniques can reduce the time necessary for flood mapping. We test various machine learning methods to find the one with the highest classification accuracy. We also present the most important points for quick and effective machine learning procedures on remote sensing data. First, the data must be prepared correctly. We use satellite images, digital terrain models (DTMs), and the river network. The data in its primary form (e.g., bands of multispectral satellite images or DTMs) is insufficient. We also need certain derived attributes, such as the vegetation index or the slope derived from the DTM. Second, we must select suitable training samples and a suitable machine learning method. This approach to determining floods is presented in a case study of flash floods in the Selška Sora river valley. Machine learning techniques have proven successful in quickly determining flooded areas. The best results are produced by the J48 decision tree algorithm. The success of the ensemble machine learning methods is comparable to the J48 algorithm, while the JRip classification is not as good.

Change detection of urban areas - the Ljubljana, Slovenia case study

In this study we apply the methodology of post-classification change detection to map and monitor land cover changes and urban expansion in wider Ljubljana region. We used multitemporal Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) images from 1992, 1999 and 2005 to produce three land cover/land use maps. Post classification comparison of these maps was used to obtain »from-to« statistics and change detection maps.

Application of In-Segment Multiple Sampling in Object-Based Classification

When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees) and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

Application of MODIS products to analyze forest phenophases in relation to elevation and distance from sea

Abstract This paper examines whether satellite images can be used to see a green wave in the small but geographically diverse territory of Slovenia. We used the phenological products of the MODIS satellite system to analyze and calculate the correlations between the onset, decrease, and duration of greenness on one hand, and the elevation and distance from the sea on the other. A statistically reliable significant correlation was primarily determined between onset of greenness increase (onset of the vegetation period) and elevation. The other correlations did not attain such a high significance. The results of the analysis using remote data sensing were confirmed by analysis of phenological data from the Slovenian Environment Agency, which collects data on leafing out and yellowing of beeches (Fagus sylvatica), which are the most widespread species in Slovenia.